Effectiveness of Macroprudential Policies and Other Measures on Dedollarization in Portfolio Dollarized Economies: The Case of Armenia

The purpose of this study is to determine the most effective policy tool, with an emphasis on macroprudential policies, for decreasing dollarization in Armenia. Armenia exhibits portfolio dollarization, meaning inflation is not the root cause (inflation levels are very low in Armenia, and typically high inflation drives dollarization). The effects on the deposit dollarization rate by a number of policies was tested through six separate regressions using Matlab and through VAR modeling in Eviews. It was determined that to decrease the rate of dollarization, the policy of keeping dollar-denominated deposits in drams should be reversed, as this is a contributor to dollarization, and that loan loss provisions for dollar loans are particularly effective in decreasing dollarization. Making use of more loan loss provisions (and making them dynamic) for dollar loans and establishment of loan to value ratio caps for dollar loans would benefit the dedollarization process in Armenia.

Acknowledgments

I would like to thank Karen Poghosyan for his enormous support throughout the entire project and for all his invaluable advice with regards to the study and the econometric analysis. I would also like to thank Armen Nurbekyan, head of the CBA research department, for his suggestions and advice throughout the study, and his extremely helpful feedback. Additionally, I am indebted to my colleagues and friends Anton Ulyanov, Farhan Javed, and Robert Tumanyan for their support and idea-brainstorming-coffee-breaks throughout the study. Also, a huge thank you to the entire research department at the CBA, who made my work and time extremely meaningful and an experience I will cherish the rest of my life. Lastly, I very grateful to the Harvard University Davis Center Goldman Grant and an OCS Grant for funding this research.

Introduction

1.1 Dollarization

The dollarization rate of a country is calculated as being the ratio of foreign currency denominated deposits to total deposits. This is often also subdivided into a deposit dollarization rate and loan or credit dollarization rate. In this paper, dollars refer to all foreign currency (in addition to dollars, other currency like euros, yen, etc., are referred to as “dollars”). As a country becomes more dollarized, the central bank loses influence over the economy—since any actions that the central bank takes are severely diluted in impact. The greatest risk is that the central bank can no longer act as a lender of last resort—a problem that plagued Argentina and Uruguay. When a central bank no longer acts as a lender of last resort, it is impossible to guarantee bank stability, which may translate to jeopardizing an entire country’s financial stability. Dollarization also means that “residents are minimally benefited by the liquidity boost to domestic financial markets” (Naceur, et. al, 2015, and Levy-Yeyato, 2006). Central Banks find regulating the economy increasing difficult with higher dollarization rates, and the actions the Federal Reserve or European Central Bank takes may often be counter to what is necessary in Armenia, or another heavily dollarized country.

In calculating dollarization rates, a major criticism is that they are largely dependent on the exchange rate. For example, setting the Armenian dram 2:1 with the dollar, with 50% of the deposits in dollars and 50% in drams—gives a 50% dollarization rate (so say 50 dollars and 100 drams). Once the country suffers a strong depreciation, even though the economy is composed of 50 dollars and 100 drams, with a 4:1 exchange rate of dram to the dollar the dollarization rate increases to 67%, a dramatic increase. In reality, no change in the in currency used has occurred in the country, though this exchange rate effects gives the illusion that dollarization has changed markedly. The more isolated a country is, the less such a change in dollarization will affect it, as domestic prices will not change.

In Latin America, historically the prime culprit has been uncontrolled inflation driving dollarization, though it is not possible to say inflation itself is the root cause—rather: “The evidence is consistent with weak institutions driving inflation, which in turn leads to greater dollarization.” (Rajan and Tokatlidis, 2005). Mwase and Kumah found that there are a number of determinants for dollarization:

Neceur et. al., state that high dollarization rates in Caucus and Central Asia (CCA) countries are due to “asymmetric exchange rate policies, inadequate prudential regulation, financial stability concerns, and idiosyncratic economic factors”. Armenia follows a free-floating exchange rate policy (free market, not government determined) (CBA, 2010), and “has achieved relatively low inflation, [and] has implemented for a number of years a comprehensive strategy to reduce macroeconomic imbalances and vulnerabilities” (Rodriguez and Manookian, 2014). It would appear that Armenia should have low dollarization, though this is not the case—Armenia has one of the highest dollarization rates in the world.

Figure 2. Rates of dollarization for loans and deposits in Armenia. During the financial crisis of 2008 deposits dollarization significantly increased. Typically, loan and deposit rates are well correlated (CBA data).

High inflation and high dollarization typically cause dollarization of “asset substitution” form, meaning transactions occur in dollars (Levy-Yeyati, 2006). This may lead to a country officially adopting the dollar, as Ecuador and Panama have done (Alvarez-Plata and García-Herrero, 2007). In cases of low inflation, weak institutions in one form or another directly drive dollarization—people choose to hold money in another currency leading to “portfolio dollarization”, and the financial system becomes overly dollarized. Foreign asset demand also rises with exchange rate depreciations and uncertainty in general economic prospects for a country. This is in line with Armenia’s rise in dollar deposits during the financial crisis.

Neaceur et. al. suggested that CCA countries should deepen their financial systems to counter dollarization. Specific examples include “introduction of local-currency-denominated securities with credible indexation systems, development of markets for instruments to hedge currency risks, enhancement of non-banking institutions and capital markets, [and] improvement of credit information systems” (2015). Neaceaur et.al. also state that “a menu of policies aimed at macroeconomic stabilization, with a complement of prudential regulations is essential for the CCA countries” (2015). It is still unclear however, which prudential regulations are most effective, and this may often be different from country to country.

1.2. Macroprudential Policies and Other Tools

The role of macroprudential policy is “to re-orient prudential regulation towards risk across the system as a whole—so called systemic risk” (Bank of England, 2009). Properly implemented, prudential regulations lower risk to a financial system by spreading the vulnerabilities out across the entire system. Macroprudential measures include a number of different reserve requirement manipulations, insurance premiums of dollarized deposits, limits on dollar lending, and strict risk-management, such as caps of loan to value ratios and debt to income caps for dollar loans, amongst other policies. By setting high reserve requirements for foreign exchange denominated deposits and low reserve requirements for local currency, banks find it more expensive to hold foreign currency, and the cost is passed on to the depositor. Thus, the high reserve requirement can act as a sort of “tax” on dollar deposits. Throughout the present study the “currency denomination of dollar deposits” variable refers to the decision undertaken by the central bank in recent years to require reserves for dollar deposits to be held in varying percentages in drams rather than the original currency in which the deposit was made. A value of 50% would mean half the required reserve value would be in drams, and half in dollars.

The government may also implement liquidity ratios, which for Armenia are a 4% floor for highly liquid assets to total assets and a 10% floor for highly liquid assets to demand liabilities. Liquidity ratios are not considered to be very effective in Armenia, as the ratios are not very strict (not binding). Despite this, Armenia appears to still have high foreign exchange liquidity (Rodriguez and Manookian, 2014). The refinancing rate is the rate the central bank charges banks to borrow money when they are short on liquidity, and influence the interest rate banks charge when offering loans. Typically, the interest rate for dollar loans is lower than for dram loans, assuming that the uncovered interest rate parity does not hold. Foreign owned banks make use of this arbitrage opportunity and drive credit dollarization in developing economies (Basso et. al., 2007). By raising the refinancing rate, interest rates for loans go up, decreasing the quantity of both dollar and dram loans—but dollar loans are more affected due to their initially lower interest rate.

In Armenia, bank deposits denominated in drams are guaranteed up to 10 million drams, and if in foreign currency, up to 5 million drams. This increases the risk of holding money in non-dram currency, but the primary motivation is to increase financial credibility, and helps deepen the financial system.

Loan to value caps and debt to value caps are both meant to manage risk. A high loan to value ratio is very risky as there is little equity and the losses for the creditor may be large in case of bankruptcy. This risk is magnified if the loan is given in dollars (for example for a mortgage) and the borrowing entity’s income is in drams. By setting a cap on this ratio risk may be decreased, helping counter dollarization. Armenia does not have such a ratio in place (CBA data). Having a cap lower for dollar loans than for dram loans helps discourage the dollar loans and internalizes some of the risk. Likewise, debt to income caps in much the same way may distinguish a risky loan from a safer one, and entities above the cap threshold will find it difficult or even impossible to borrow more money without first increasing revenue or decreasing outstanding debts. Since 2007, this has been 50% in Armenia (CBA, 2007). As with loan to value caps, setting a cap for dollar denominated debts to be lower than for than for drams can help fight dollarization. In addition, it may make sense for banks to take into account the currency denomination of the borrower’s income. Some countries sometimes put in place regulations that make it difficult for non-exporters to receive foreign currency loans.

Loan loss provisioning works by creating a cushion for expected losses. During boom cycles, provisions are increased, decreasing profits due to the “expected” default of a loan in the future. This may provide a buffer that weakens the impact of bust cycles, during which the defaults would occur. During the bust cycles only actual expenses remain, which helps increasing the dwindling profits. Provisions also apply to other sectors, and act in much the same way by accounting “expected expenses” for ordinary businesses during boom cycles to lessen the impact of bust cycles. While in Armenia provisioning is in place, its lack of movement makes it not very dynamic and therefore not as effective. Setting the provision rates to be higher for dollar than for dram loans (as Armenia currently does) helps make dollar loans more costly to the bank and discourages dollar loans.

1.3. Objectives

The primary objective of this work is to establish the effectiveness of macroprudential and other policies in combating portfolio dollarization, specifically in Armenia. This work may then be expanded as a cross section to include other counties facing high portfolio dollarization, such as Georgia and Tajikistan.

Methodology

2.1. Data

Data was retrieved from CBA sources and literature. A Hodrick–Prescott filter with smoothing parameter lambda 14400 was applied to the time-series data and the trend difference was taken. This step was to remove cyclical variation and establish long-term trends in the monthly data. Graphs showing the raw data before any detrending or smoothing can be observed below (figure 3.)

Figure 3 (7 graphs). Time series data from January 2006 to May 2016 of the variables examined (125 observations) (CBA data and author’s calculations).

2.2. Regressions

Regressions using the Ordinary Least Squares (OLS) and Least Absolute Deviations (LAD) methods were performed in Matlab using code developed by James LeSage for the Matlab Econometrics Toolbox. Robust regressions using the Huber’s t function, Ramsay’s E function, Andrew’s wave function, and Tukey’s biweight methods were also performed. Dollarization rates for deposits were used (according to Levy-Yeyati credit dollarization changes typically follow deposit dollarization changes (2003)). For the case of Armenia, we may observe Levy-Yeyati’s claim below (figure 4).

Figure 4. Correlation of dollarization in deposits and loan in Armenia. (data from January 2006 to May 2016, CBA sources).

The effects of the refinancing rate, the foreign exchange rate, the dram denominated deposits reserve requirement, the dollar denominated deposits reserve requirement, the loan loss provisioning rate, and the currency denomination of dollar reserves were assessed on the deposit dollarization rate. This was analyzed by comparing the beta values as calculated in Matlab. These results are found in Tables 1 and 2. Certain macroprudential measures, such as capital requirements or debt to income ratios were not used, as they have been constant since 2006, the start of this study.

The refinancing rate and loan-loss provision rate are both negative, meaning that as either increases dollarization decreases—a sign of successful policy. With a higher refinancing rate, interest rates on loans go up, and the quantity of both dram and dollar loans decreases. However, because dollar loans initially were subject to lower interest rates, they increase more, meaning that as a result a higher percentage of loans are denominated in drams. With the loan-loss provision rate (specifically for dollar loans) increases, banks must keep more funds is specifically designated provision accounts, leading to fewer dollars being available for loans, also decreasing dollarization.

As the exchange rate variable increases dollarization increases—this too follows expectation, as the exchange rate increase means a dram depreciation. In an effort to maintain purchasing power consumers hold dollars, which is evident with the increased dollarization. The most counterintuitive result is that an increased reserve requirement for dollar denominated deposits leads to an increase in dollarization. This appears to go against the objectives of the higher reserve requirement. This may be explained by the behavior of the “percentage of reserves denominated in original currency for dollar denominated deposits” variable. As dollar deposits are increasingly converted by banks in drams (a certain percentage of the deposits for the reserves), as regulations require, banks are left with an excess of dollars, which they begin to offer at increasingly competitive rates for loans. This leads to an increase in credit dollarization, as more consumers are receiving dollar loans. At the same time, less loans are being given in drams as more drams are being relocated to be held as reserves instead of being available for loans. Rodriguez, P. and Manookian, A. (2014) claimed a similar phenomenon in their report. This effect is so strong that increased reserve requirements for dollar deposits leads to more dollarization, since now even more dollars must be converted into drams, and even fewer drams are available for loans.

The positive correlation of increased reserve requirements for dram denominated deposits is expected and may be explained the following way: if banks are required to keep a larger percentage of their drams as reserve, they lend out less drams in loans. This decreases the percentage of loans that are in drams, increasing dollarization. These results are generally consistent across all the regressions performed, whose results may be seen below (Table 2):

A Bayesian Vector Auto Regression (VAR) was performed to establish the shock effect of the various polices on the rate of deposit dollarization. Using Eviews, the Bayesian VAR model was built using 1 lag and a Litterman/Minnesota prior. All data had a Hodrick–Prescott filter applied and the trend difference was taken before modeling to establish stationarity. Impulse responses (figures 5-10) (including accumulated responces (figures 11-16)) were then made a one-unit decomposition over a period of 60 months.

Responses to Nonfactored One Unit Innovations

Figure 5. Impulse response of the dollarization rate to a 1% increase in the refinancing rate.

Figure 6. Impulse response of the dollarization rate to a 1% increase in the foreign exchange rate.

Figure 7. Impulse response of the dollarization rate to a 1% increase in the dram denominated deposit reserve requirement.

Figure 8. Impulse response of the dollarization rate to a 1% increase in the dollar denominated deposit reserve requirement.

Figure 9. Impulse response of the dollarization rate to a 1% increase in the loan-loss provisioning rate for dollar loans.

Figure 10. Impulse response of the dollarization rate to a 1% increase in the percentage of the reserve requirements for dollar denominated deposits that must be denominated in drams.

Accumulated Response to Nonfactored One Unit Innovations

Figure 11. Accumulated impulse response of the dollarization rate to a 1% increase in the refinancing rate over 60 months.

Figure 12. Accumulated impulse response of the dollarization rate to a 1% increase in the foreign exchange rate over 60 months.

Figure 13. Accumulated impulse response of the dollarization rate to a 1% increase in the dram denominated deposit reserve requirement over 60 months.

Figure 14. Accumulated impulse response of the dollarization rate to a 1% increase in the dollar denominated deposit reserve requirement over 60 months.

Figure 15. Accumulated impulse response of the dollarization rate to a 1% increase in the loan-loss provisioning rate over 60 months.

Figure 16. Impulse response of the dollarization rate to a 1% change in the percentage of the reserve requirements for dollar denominated deposits that must be denominated in drams over 60 months.

Besides the exchange rate, the greatest shock on the dollarization rate is from the currency denomination of reserve requirements. This policy increases dollarization, as do increases in the dollar denominated deposit reserve requirements and the exchange rate. All VAR results are consistent with those calculated in the earlier regressions.

Conclusions and Policy Recommendations

This study examined the effects of macroprudential policy and other tools on dollarization in Armenia. Through 6 different Matlab regression tests and a construction of a Bayesian VAR model the most effective policy was determined for Armenia, and it was also found that due to the currency denomination of reserve requirements the tools did not always work as intended. Loan loss provisions were one of the most useful tools, while increasing the dollar denominated deposit reserve requirement was counterproductive due to currency denomination requirements for foreign currency denominated deposit reserves.

To best counter dollarization in Armenia, the following steps are recommended based on this study:

If Armenia is not facing the economic conditions that followed the 2008 financial crisis, the policy of keeping foreign currency denominated deposits in drams should be terminated and gradually phased out. This is a significant driver of dollarization. Dollar denominated deposits reserves should be kept in dollars. In fact, it may even make sense to keep dram denominated deposit requirements in dollars, but further study is required.

Establish stricter loan-loss provisioning rates, and make them more dynamic. Specifically, it is crucial that the provisioning rate be higher for dollar denominated loans.

Based off of literature, other steps could potentially include establishment of stricter liquidity ratios, establishment of loan-to-value ratio caps (with a lower cap for dollar loans), and perhaps forcing dollarization through prohibition of dollar loans for major purchases such as mortgages and automobiles for borrowers whose earnings are not in dollars. While this particular study did not target these variables, international experience suggests Armenia would benefit from a wider dedollarization toolbox.

Possible Future Steps, Challenges, and Limitations

Future expansion of this study should include more testable variables, such as inflation, government debt, and measures of uncertainly and risk aversion, such as VIX. A cross sectional study examining these effects for many countries (especially those in a similar financial position like Georgia or Tajikistan) would be extremely useful.

One of the greatest challenges for this study was data collection, as certain data that relates to the financial system is extremely difficult to gain access to. Additionally, as a non-Armenian speaker, much of the data was originally in Armenian and the data search and translate process complicated the study. Since the study only concerned Armenia, the results should not be extrapolated to other countries. In addition, since there was a limited number of variables tested and dollarization depends on the interaction of all possible variables and the hard-to-quantify “animal spirits”, there may be important variables that have not been accounted for. Additionally while different economic policy implementations have been correlated to the dollarization rate, a single variable itself changing may not be the cause of changes in dollarization but simply reflect other simultaneous economic developments. It may also be the case that while a policy is established at the government level it is not actually implemented, or not as perfectly as we would expect from the government policy, but this data is also classified as it would require microdata of banking entities.